Multi-objective optimization of the HVAC (heating, ventilation, and air conditioning) system performance

A data-driven approach to optimize the total energy consumption of the HVAC (heating, ventilation, and air conditioning) system in a typical office facility is presented. A multi-layer perceptron ensemble is selected to build the total energy model integrating three indoor air quality models, the facility temperature model, the facility relative humidity model, and the facility CO2 concentration model. To balance the energy consumption and the indoor air quality, a quad-objective optimization problem is constructed. The problem is solved with a modified particle swarm optimization algorithm producing control settings of supply air temperature and static pressure of the air handling unit. By assigning different weights to the objectives to the model, the generated control settings optimize HVAC system with the trade-off between the energy consumption and the facility thermal comfort. Significant energy savings can be obtained even with air quality constraint. •A data driven approach to optimize HVAC system.•Data-mining algorithms are used and compared to build the energy and air quality models.•A quad-objective optimization model is constructed and solved with a modified PSO algorithm.•Significant energy saving can be obtained even considering the balance of air quality.